The Bi-Functional Organization of Human Basement Membranes

The current basement membrane (BM) model proposes a single-layered extracellular matrix (ECM) sheet that is predominantly composed of laminins, collagen IVs and proteoglycans. The present data show that BM proteins and their domains are asymmetrically organized providing human BMs with side-specific properties: A) isolated human BMs roll up in a side-specific pattern, with the epithelial side facing outward and the stromal side inward. The rolling is independent of the curvature of the tissue from which the BMs were isolated. B) The epithelial side of BMs is twice as stiff as the stromal side, and C) epithelial cells adhere to the epithelial side of BMs only. Side-selective cell adhesion was also confirmed for BMs from mice and from chick embryos. We propose that the bi-functional organization of BMs is an inherent property of BMs and helps build the basic tissue architecture of metazoans with alternating epithelial and connective tissue layers

Copper coordination polymers from cavitand ligands: hierarchical spaces from cage and capsule motifs, and other topologies

The cyclotriveratrylene-type ligands (±)-tris(iso-nicotinoyl)cyclotriguaiacylene L1 (±)-tris(4-pyridylmethyl)cyclotriguaiacylene L2 and (±)-tris{4-(4-pyridyl)benzyl}cyclotriguaiacylene L3 all feature 4-pyridyl donor groups and all form coordination polymers with CuI and/or CuII cations that show a remarkable range of framework topologies and structures. Complex [CuI4CuII1.5(L1)3(CN)6]·CN·n(DMF) 1 features a novel 3,4-connected framework of cyano-linked hexagonal metallo-cages. In complexes [Cu3(L2)4(H2O)3]·6(OTf)·n(DMSO) 2 and [Cu2(L3)2Br2(H2O)(DMSO)]·2Br·n(DMSO) 3 capsule-like metallo-cryptophane motifs are formed which linked through their metal vertices into a hexagonal 2D network of (43.123)(42.122) topology or a coordination chain. Complex [Cu2(L1)2(OTf)2(NMP)2(H2O)2]·2(OTf)·2NMP 4 has an interpenetrating 2D 3,4-connected framework of (4.62.8)(62.8)(4.62.82) topology with tubular channels. Complex [Cu(L1)(NCMe)]·BF4·2(CH3CN)·H2O 5 features a 2D network of 63 topology while the CuII analogue [Cu2(L1)2(NMP)(H2O)]·4BF4·12NMP·1.5H2O 6 has an interpenetrating (10,3)-b type structure and complex [Cu2(L2)2Br3(DMSO)]·Br·n(DMSO) 7 has a 2D network of 4.82 topology. Strategies for formation of coordination polymers with hierarchical spaces emerge in this work and complex 2 is shown to absorb fullerene-C60 through soaking the crystals in a toluene solution

Index to NASA Tech Briefs, January - June 1967

Technological innovations for January-June 1967, abstracts and subject inde

얼굴 표정 인식, 나이 및 성별 추정을 위한 다중 데이터셋 다중 도메인 다중작업 네트워크

학위논문(석사)--서울대학교 대학원 :공과대학 전기·정보공학부,2019. 8. Cho, Nam Ik.컨볼 루션 뉴럴 네트워크 (CNN)는 얼굴과 관련된 문제를 포함하여 많은 컴퓨터 비전 작업에서 매우 잘 작동합니다. 그러나 연령 추정 및 얼굴 표정 인식 (FER)의 경우 CNN이 제공 한 정확도는 여전히 실제 문제에 대해 충분하지 않습니다. CNN은 얼굴의 주름의 두께와 양의 미묘한 차이를 발견하지 못했지만, 이것은 연령 추정과 FER에 필수적입니다. 또한 실제 세계에서의 얼굴 이미지는 CNN이 훈련 데이터에서 가능할 때 회전 된 물체를 찾는 데 강건하지 않은 회전 및 조명으로 인해 많은 차이가 있습니다. 또한 MTL (Multi Task Learning)은 여러 가지 지각 작업을 동시에 효율적으로 수행합니다. 모범적 인 MTL 방법에서는 서로 다른 작업에 대한 모든 레이블을 함께 포함하는 데이터 집합을 구성하는 것을 고려해야합니다. 그러나 대상 작업이 다각화되고 복잡해지면 더 강력한 레이블을 가진 과도하게 큰 데이터 세트가 필요할 수 있습니다. 따라서 원하는 라벨 데이터를 생성하는 비용은 종종 장애물이며 특히 다중 작업 학습의 경우 장애가됩니다. 따라서 우리는 가버 필터와 캡슐 기반 네트워크 (MTL) 및 데이터 증류를 기반으로하는 다중 작업 학습에 기반한 새로운 반 감독 학습 방법을 제안한다.The convolutional neural network (CNN) works very well in many computer vision tasks including the face-related problems. However, in the case of age estimation and facial expression recognition (FER), the accuracy provided by the CNN is still not good enough to be used for the real-world problems. It seems that the CNN does not well find the subtle differences in thickness and amount of wrinkles on the face, which are the essential features for the age estimation and FER. Also, the face images in the real world have many variations due to the face rotation and illumination, where the CNN is not robust in finding the rotated objects when not every possible variation is in the training data. Moreover, The Multi Task Learning (MTL) Based based methods can be much helpful to achieve the real-time visual understanding of a dynamic scene, as they are able to perform several different perceptual tasks simultaneously and efficiently. In the exemplary MTL methods, we need to consider constructing a dataset that contains all the labels for different tasks together. However, as the target task becomes multi-faceted and more complicated, sometimes unduly large dataset with stronger labels is required. Hence, the cost of generating desired labeled data for complicated learning tasks is often an obstacle, especially for multi-task learning. Therefore, first to alleviate these problems, we first propose few methods in order to improve single task baseline performance using gabor filters and Capsule Based Networks , Then We propose a new semi-supervised learning method on face-related tasks based on Multi-Task Learning (MTL) and data distillation.1 INTRODUCTION 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Age and Gender Estimation . . . . . . . . . . . . . . . . . . 4 1.2.2 Facial Expression Recognition (FER) . . . . . . . . . . . . . 4 1.2.3 Capsule networks (CapsNet) . . . . . . . . . . . . . . . . . . 5 1.2.4 Semi-Supervised Learning. . . . . . . . . . . . . . . . . . . . 5 1.2.5 Multi-Task Learning. . . . . . . . . . . . . . . . . . . . . . . 6 1.2.6 Knowledge and data distillation. . . . . . . . . . . . . . . . . 6 1.2.7 Domain Adaptation. . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2. GF-CapsNet: Using Gabor Jet and Capsule Networks for Face-Related Tasks 10 2.1 Feeding CNN with Hand-Crafted Features . . . . . . . . . . . . . . . 10 2.1.1 Preparation of Input . . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 Age and Gender Estimation using the Gabor Responses . . . . 13 2.2 GF-CapsNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1 Modification of CapsNet . . . . . . . . . . . . . . . . . 16 3. Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasks 20 3.1 MTL learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2 Data Distillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4. Experiments and Results 25 4.1 Experiments on GF-CNN and GF-CapsNet . . . . . . . . . . . . . . 25 4.2 GF-CNN Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2.1 GF-CapsNet Results . . . . . . . . . . . . . . . . . . . . . . 30 4.3 Experiment on Distill-2MD-MTL . . . . . . . . . . . . . . . . . . . 33 4.3.1 Semi-Supervised MTL . . . . . . . . . . . . . . . . . . . . . 34 4.3.2 Cross Datasets Cross-Domain Evaluation . . . . . . . . . . . 36 5. Conclusion 38 Abstract (In Korean) 49Maste

Adaptation of a visualized loop-mediated isothermal amplification technique for field detection of Plasmodium vivax infection

<p>Abstract</p> <p>Background</p> <p>Loop-mediated isothermal amplification (LAMP) is a high performance method for detecting DNA and holds promise for use in the molecular detection of infectious pathogens, including <it>Plasmodium </it>spp. However, in most malaria-endemic areas, which are often resource-limited, current LAMP methods are not feasible for diagnosis due to difficulties in accurately interpreting results with problems of sensitive visualization of amplified products, and the risk of contamination resulting from the high quantity of amplified DNA produced. In this study, we establish a novel visualized LAMP method in a closed-tube system, and validate it for the diagnosis of malaria under simulated field conditions.</p> <p>Methods</p> <p>A visualized LAMP method was established by the addition of a microcrystalline wax-dye capsule containing the highly sensitive DNA fluorescence dye SYBR Green I to a normal LAMP reaction prior to the initiation of the reaction. A total of 89 blood samples were collected on filter paper and processed using a simple boiling method for DNA extraction, and then tested by the visualized LAMP method for <it>Plasmodium vivax </it>infection.</p> <p>Results</p> <p>The wax capsule remained intact during isothermal amplification, and released the DNA dye to the reaction mixture only when the temperature was raised to the melting point following amplification. Soon after cooling down, the solidified wax sealed the reaction mix at the bottom of the tube, thus minimizing the risk of aerosol contamination. Compared to microscopy, the sensitivity and specificity of LAMP were 98.3% (95% confidence interval (CI): 91.1-99.7%) and 100% (95% CI: 88.3-100%), and were in close agreement with a nested polymerase chain reaction method.</p> <p>Conclusions</p> <p>This novel, cheap and quick visualized LAMP method is feasible for malaria diagnosis in resource-limited field settings.</p